modeling cross-contamination in quantitative microbial risk assessment

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1st International Conference on Microbial Risk Asse ssment: Foodborne Hazards, College Park MD, July 20 02 1 of 19 Modeling Cross- contamination in Quantitative Microbial Risk Assessment Don Schaffner Food Risk Analysis Initiative Rutgers University

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Modeling Cross-contamination in Quantitative Microbial Risk Assessment. Don Schaffner Food Risk Analysis Initiative Rutgers University. The Achilles heel of risk assessment - G. Paoli 7/24/02. Modeling Cross-contamination in Quantitative Microbial Risk Assessment. - PowerPoint PPT Presentation

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Page 1: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Modeling Cross-contamination in

Quantitative Microbial Risk Assessment

Don SchaffnerFood Risk Analysis Initiative

Rutgers University

Page 2: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Modeling Cross-contamination in

Quantitative Microbial Risk Assessment

Don SchaffnerFood Risk Analysis Initiative

Rutgers University The Achilles heel of risk assessment- G. Paoli 7/24/02

Page 3: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Laboratory Experiments

• Nalidixic acid resistant Enterobacter aerogenes with attachment characteristics similar to Salmonella

• More than 30 participants dice inoculated chicken, wash hands and/or wear gloves, slice lettuce

• Sample hands, foods, faucet spigots cutting boards for Enterobacter

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1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Why these studies ?

• Practical consideration– A company was interested in

showing efficacy of a touch-free faucet… they provided funding!

• Our research philosophy– Variability matters, especially for

modeling and risk assessment

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Things to think about…

• A surface can either …– be sampled or – be used to contaminate another surface

• Relative numbers and rates– Dirty hands -> clean faucet handles– Dirty hands <-> dirty faucet handles– Clean hands <- dirty faucet handles

• How many observations at one set of conditions are needed?

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Data Analysis

• Log transformation of % transfer• Frequency histogram in Excel• Best distribution using BestFit• Normal distributions fit the data

100Hand on CFU

Spigot on CFU(%) rate Transfer

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Think about data transformation…

Percent Transfer

0 25 50 75 100

Fre

quen

cy

0

2

4

6

8

Log Percent Transfer

-2 -1 0 1 2

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Cross contamination results

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1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Glove barrier: Chicken to hand

Page 10: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Our Published Work

• Chen, Y., Jackson, K.M. Chea, F.P. and Schaffner, D.W. 2001. Quantification and variability analysis of bacterial cross-contamination rates in the kitchen. Journal of Food Protection. 64(1):72-80.

• Montville, R., Chen, Y., and Schaffner, D.W. 2001. Glove barriers to bacterial cross-contamination. Journal of Food Protection. 64(6), 845–849.

• Montville, R., Chen, Y. and Schaffner, D.W., 2002. Risk assessment of handwashing efficacy using literature and experimental data. International Journal of Food Microbiology 73(2-3), 305-313.

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I nvestigator Organization Project

Griffi th University of Wales Behavior f requencies, high risk surf aces

J aykus North Carolina State University

Cross contamination with viruses and pathogenic bacteria

Kasuga National I nstitute of I nf ectious Diseases

Cross contamination with naturally occurring bacteria, diff erent f oods

Mattick University of Bristol Dishwashing eff ectiveness

Michaels Georgia Pacifi c Handwashing and cross contamination

Sobsey Univ NC, Chapel Hill Simultaneous Serratia and Phage transf er

Todd Michigan State Cross contamination with Listeria

Currently ongoing research with application on microbial behavior in the kitchen environment

Page 12: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

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Other recent publications

• L. L. Gibson, J. B. Rose, C. N. Haas, C. P. Gerba, and P. A. Rusin. Quantitative assessment of risk reduction from hand washing with antibacterial soaps. J.Appl.Microbiol. 92:136S-143S, 2002.– “The objective of this study was to examine the risk reduction

achieved from using different soap formulations after diaper changing using a microbial quantitative risk assessment approach.”

• T. A. Cogan, J. Slader, S. F. Bloomfield, and T. J. Humphrey. Achieving hygiene in the domestic kitchen: the effectiveness of commonly used cleaning procedures. J.Appl.Microbiol. 92 (5):885-892, 2002.– “Aims: To quantify the transmission of Salmonella and Campylobacter

to hands, cloths, and hand- and food-contact surfaces during the preparation of raw poultry in domestic kitchens, and to examine the impact on numbers of these bacteria of detergent-based cleaning alone, or in conjunction with thorough rising.”

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Things to consider in QMRA

• Cross-contamination must be handled differently than other increases– Two log increase due to growth: 1 + 2 =

3– 100 CFU added from cross-

contamination: 10 + 100 = 110

• Modeling the non-linear nature of the process

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1st International Conference on Microbial Risk Assessment: Foodborne Hazards, College Park MD, July 2002

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Non-linear process

Raw chickenInitial Cooked chicken

Cutting board

Hand

Lettuce

Exposure

Storage effectlog increase

Xcontam rateraw to board

Cooking effectlog decrease

Xcontam ratehand to mouth

Xcontam rateboard to lettuce

Xcontam rateraw to hand

Xcontam ratehand to cooked

Xcontam ratehand to lettuce

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Model interfaceModel

Initial 100

Storage effect log increase 2

Cooking effect log decrease 5

Log Exposure Calc

Log exposure stats Calc

Xratemodule

xrate mean -2

xrate sd 1

Exp where less than 1 is zero Calc

Page 16: Modeling Cross-contamination in Quantitative Microbial Risk Assessment

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High Rate Resultsinitial: 1000storage increase: 1cooking decrease: 5log cross contamination rate mean: -1 (10%)log cross contamination standard deviation: 1

Pro

bab

ilit

y D

ensi

ty

Log Exposure-2.5 0 2.5 5 7.5 10

0

0.5

1

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Low Rate Results

initial: 1000storage increase: 1cooking decrease: 5log cross contamination rate mean: -3 (0.1%)log cross contamination standard deviation: 1

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Where to we go from here?

• What factors are important in controlling transfer rate?– Soil type, organism, pressure, concentration, etc.

• What routes are important?– Hand to mouth, cutting board to raw product, etc.

• What behaviors are important?– Handwashing, cleaning frequency, etc.

• Once we know what’s important, we can ignore what’s not important, and include a useful, simplified cross contamination module in our risk assessments

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Acknowledgements

• Lee Budd for stimulating discussions• Sloan Valve company for support and

funding• The Food Risk Analysis Initiative was

funded in part by the New Jersey Agricultural Experiment Station

• Members of the FoRAI team: Yuhuan Chen, Rebecca Montville, Kristin Jackson, Siobain Duffy, Purvi Vora, Lihui Zhao